WHAT MAKES A GREAT JAMES BOND MOVIE?

Introduction

Whether this is a great number or peculiar gadgets or glasses of cold vodka Martni, there are definitely many aspects constituting a great James Bond movie. Having watched A spy who loved me (1977) recently I’ve begun to wonder if that could be somehow assessed by the help of machine learning. This is my small summer data science project that aims to predict the average IMDB score for the newest 007 movie No time to die and see what contributes to the score.

More formally, we will be looking at a regression problem for a small dataset (24 items) and training a random forest using cross validation. Then we will examine the breakdown plot for a previously unseen example - the new movie. I will be using caret package for the model and DALEX for explanation.

1. The data: For your eyes only

data(JamesBond)
df <- JamesBond %>% select(-US_Gross, -World_Gross, -US_Adj, -Budget, -Avg_User_Rtn_Tom) %>%
  relocate(Avg_User_IMDB, .after = last_col())

I will be using the James Bond dataset from the HoRM package. See resources
Here we have the data of the previous 24 movies. I’m going to use 12 ouf of the 18 available features, namely: Year, Movie, Bond, World_Adj, Budget_Adj, Film_Length, Conquests, Martinis, BJB, Kills_Bond, Kills_Others, Top_100, Avg_User_IMDB.
To clear out the feature names:
- World_Adj The film’s 2013-adjusted worldwide gross (in 1000’s of U.S. dollars).
- Budget_Adj The film’s 2013-adjusted budget (in 1000’s of U.S. dollars).
- Avg_User_IMDB The average user rating on IMDB (www.imdb.com).
- Conquests The number of “conquests” by Bond in the film.
- Martinis The number of martinis Bond drank in the film.
- BJB The number of times Bond stated “Bond. James Bond.” in the movie.
- Kills_Bond The number of people killed by Bond.
- Kills_Others The number of people killed in the film by people other than Bond.
- Top_100 An indicator where a value of 1 means the title song within the top 100 on the UK Singles Chart and the U.S. Billboard Hot 100 and a value of 0 means it did not.

kable(df)

Year	Movie	Bond	World_Adj	Budget_Adj	Film_Length	Conquests	Martinis	BJB	Kills_Bond	Kills_Others	Top_100	Avg_User_IMDB
1962	Dr. No	Sean Connery	457928	7688	110	3	2	1	4	8	0	7.3
1963	From Russia with Love	Sean Connery	598624	15174	115	4	0	0	11	16	0	7.5
1964	Goldfinger	Sean Connery	935404	22468	110	2	1	2	9	68	1	7.8
1965	Thunderball	Sean Connery	1040693	66333	130	3	0	0	20	90	1	7.0
1967	You Only Live Twice	Sean Connery	775740	66035	117	3	1	0	21	175	1	6.9
1969	On Her Majesty’s Secret Service	George Lazenby	518736	50608	142	3	1	2	5	37	0	6.8
1971	Diamonds Are Forever	Sean Connery	664969	41274	120	1	0	1	7	42	1	6.7
1973	Live and Let Die	Roger Moore	846046	36603	121	3	0	1	8	5	1	6.8
1974	The Man with the Golden Gun	Roger Moore	459623	32965	125	2	0	2	1	5	0	6.7
1977	The Spy Who Loved Me	Roger Moore	710290	53636	125	3	1	1	31	116	1	7.1
1979	Moonraker	Roger Moore	672514	99134	126	3	1	1	12	69	0	6.2
1981	For Your Eyes Only	Roger Moore	498812	71514	127	2	0	2	18	36	1	6.8
1983	Octopussy	Roger Moore	437059	64102	131	2	0	1	15	43	1	6.5
1985	A View to a Kill	Roger Moore	329322	64730	131	4	0	2	5	57	1	6.2
1987	The Living Daylights	Timothy Dalton	390758	81749	130	2	2	1	13	29	0	6.7
1989	License to Kill	Timothy Dalton	292392	78637	133	2	1	1	10	13	0	6.5
1995	Goldeneye	Pierce Brosnan	542985	91404	130	2	1	1	47	25	0	7.2
1997	Tomorrow Never Dies	Pierce Brosnan	491098	159117	119	3	1	1	30	24	0	6.4
1999	The World Is Not Enough	Pierce Brosnan	504091	188130	128	3	1	2	27	43	0	6.3
2002	Die Another Day	Pierce Brosnan	557433	183255	133	2	2	1	31	20	1	6.0
2006	Casino Royale	Daniel Craig	686784	117465	144	2	3	1	11	11	1	7.9
2008	Quantum of Solace	Daniel Craig	638035	248014	106	1	6	0	16	15	1	6.7
2012	Skyfall	Daniel Craig	1120980	202240	143	3	1	1	26	26	1	7.8
2015	Spectre	Daniel Craig	864553	240803	148	3	1	1	30	205	1	6.8

2. Charts: Live and let die

In this section we will look at some interesting features. Hover over the bullet marker to get extra information about the movie.

plot_df <- JamesBond
plot_df$Movie <- factor (plot_df$Movie , levels = plot_df$Movie) # we want to preserve the dataframe's chronological movie ordering

m <- list(
  l = 100,
  r = 50,
  b = 80,
  t = 150,
  pad = 2
)

fig <- plot_ly(plot_df, x = ~Year, y = ~Martinis, type= 'scatter', mode= 'lines+markers',
               line = list(color = 'rgba(49,130,189, 1)', width = 4),
               marker = list(color = 'rgba(49,130,189, 1)', size = 12),
               hoverinfo = 'text',
               text = ~paste(Movie, Year)) %>%
  layout(title = "The number of Martini drinks Bond consumed in the movie",
         font=list(size = 20),
         margin = m,
         xaxis = list(
           showgrid = FALSE,
           zeroline = FALSE,
           tickfont = list(size = 16)
         ),
         yaxis = list(
           showgrid = FALSE,
           zeroline = TRUE 
         ))
fig

plot_df <- JamesBond
plot_df$Movie <- factor (plot_df$Movie , levels = plot_df$Movie) # we want to preserve the dataframe's chronological movie ordering

fig2 <- plot_ly(plot_df, x = ~Year, y = ~Kills_Bond, name = 'By Bond', type= 'scatter', mode= 'lines+markers',
               line = list(color = 'rgba(102,0,0,1)', width = 4),
               marker = list(color = 'rgba(102,0,0,1)', size = 12),
               hoverinfo = 'text',
               text = ~paste(Movie, Year)) %>%
  add_trace(y = ~Kills_Others,  type = 'scatter', mode= 'lines+markers',
               name = "By Others",
               line = list(color = 'middle gray', width = 4),
               marker = list(color = 'middle gray', size = 12)) %>%
  layout(title = "The number of kills",
         font=list(size = 20),
         margin = m,
         xaxis = list(
           showgrid = FALSE,
           zeroline = FALSE,
           tickfont = list(size = 16)
         ),
         yaxis = list(
           title.text = '',
           showgrid = FALSE,
           zeroline = TRUE
         ))
fig2

The most correlated feature with the average IMDB score is the World gross, which should be expected. It is interesting to note that Kills, Film Length and Budget are growing with Year.

fig_corr <- JamesBond %>% select(-US_Gross, -World_Gross, -US_Adj, -Budget, -Avg_User_Rtn_Tom, -Movie, -Bond) %>%
  relocate(Avg_User_IMDB, .after = last_col()) %>%
  cor() %>% 
  ggcorrplot(method = 'square', type = 'upper', outline.color = 'white', lab_size = 8,
             title= "Feature Correlations",
             legend.title = "Corelation",
             tl.cex = 18,
             lab=TRUE) +
  theme( text = element_text(size = 22))

fig_corr

3. Regression: The world is not enough

It is time to train our regressor. We will use random forest with cross-validation. Next, we will create a DALEX explainer to examine the Feature Importance.

data(JamesBond)
df <- JamesBond %>% select(-Movie, -US_Gross, -World_Gross, -US_Adj, -Budget, -Avg_User_Rtn_Tom) %>%
  relocate(Avg_User_IMDB, .after = last_col())

train_control <- trainControl(method = "cv", number = 8, savePredictions = "all")

model_rf <- train(Avg_User_IMDB~. , data = df, method = "rf", ntree = 100, trControl = train_control)

explainer_rf <- DALEX::explain(model = model_rf,
                              label = "rf",
                              verbose = FALSE,
                              y = df$Avg_User_IMDB
                              )

print(model_performance(explainer_rf))

## Measures for:  regression
## mse        : 0.05157403 
## rmse       : 0.2270992 
## r2         : 0.7990078 
## mad        : 0.1360167
## 
## Residuals:
##          0%         10%         20%         30%         40%         50% 
## -0.38593333 -0.30377500 -0.19552000 -0.12311000 -0.07096667 -0.03891667 
##         60%         70%         80%         90%        100% 
## -0.01014000  0.07145333  0.13466333  0.31706333  0.49035000

plot(model_parts(explainer_rf, loss_function = loss_root_mean_square)) + theme(text = element_text(size = 20))

4. Break down: No time to die

I found the necessary statistics from No time to die (2021). As some were hard to find, I assumed the values of Martinis and BJBs.The gross and budget were adjusted to 2013 for consistency:

no_time_to_die <- data.frame(2021, "Daniel Craig", 608510000, 214930000, 163, 0, 1, 1, 66, 109, 1, 0)
colnames(no_time_to_die) <- colnames(df)
df <- rbind(df, no_time_to_die)

kable(no_time_to_die)

Year	Bond	World_Adj	Budget_Adj	Film_Length	Conquests	Martinis	BJB	Kills_Bond	Kills_Others	Top_100	Avg_User_IMDB
2021	Daniel Craig	608510000	214930000	163	0	1	1	66	109	1	0

predict(explainer_rf, tail(df, n=1))

##       25 
## 7.347983

pb = predict_parts(explainer = explainer_rf, tail(df, n=1), type= "break_down")
plot(pb)

We can examine the contribution of each feature of our example, the 25th 007 movie. In fact, we are very close to the true average IMDB rating! (https://www.imdb.com/title/tt2382320/, August 2022).

Please note that we used a very small dataset.

5. Resources

James Bond R dataset https://search.r-project.org/CRAN/refmans/HoRM/html/JamesBond.html

How to use DALEX with caret https://htmlpreview.github.io/?https://github.com/ModelOriented/DALEX-docs/blob/master/vignettes/DALEX_caret.html

R Markdown Cookbook https://bookdown.org/yihui/rmarkdown-cookbook/

EMA book https://ema.drwhy.ai/

Daniel Craig’s retrospective https://youtu.be/2oZdJrph3RA